Recently, there has been a paradigm shift in how the treatment of diseases is approached. There is now a focus on the uniqueness of the individual rather than application of a universal treatment strategy (Beaudet et al. 1998). This approach is described as “precision medicine”, and has seeped into the mindset of clinicians and scientists over the past few years. This switch can mainly be attributed to advances in genome sequencing, which have expanded our understanding of various diseases. Particularly in cancer, we now have the tools to map and identify distinct genetic mutations that drive tumor progression. This has led to the knowledge that individuals with seemingly the same cancer type do not respond similarly to the same therapy, indicating that the genetic makeup of an individual influences their response to treatment. Further understanding of this caveat to effective therapy is the focus of the fields of pharmacogenetics and genomic medicine. Both disciplines take into account an individual’s unique genomic information to determine their response to certain medicines or to drive therapeutic selection.
Implementation of precision medicine in the clinic is slowly progressing, and scientists are working on ways to make the transition from bench to bedside. The application of genome sequencing for selecting effective therapy can be demonstrated by research into triple negative breast cancer (TNBC). This breast cancer subtype is defined by lack of the breast cancer prognostic markers estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER-2/neu). Approximately 11-17% of breast cancers fall into this category, which is more prevalent in young individuals and patients of African descent (Kwan et al. 2009). Genome sequencing of TNBC tumor biopsies identified mutations in the epidermal growth factor receptor (EGF R) gene, a member of the receptor tyrosine kinase family, indicating that TNBC patients could benefit from tyrosine kinase inhibitor therapy (Hui-Fang Teng et al. 2011). Other studies demonstrating the overexpression of EGF R in TNBC cells (Corkery et al. 2009) led to a clinical trial testing the efficacy of the anti-EGF R monoclonal antibody cetuximab, traditionally used for the treatment of metastatic colon cancers, in TNBC (Baselga et al. 2013). A combination therapy of cetuximab and the chemotherapy drug cisplatin (an agent inducing highly toxic DNA-interstrand cross-links) resulted in increased patient survival, compared to cisplatin treatment alone (Baselga et al. 2013). This exemplifies how genomic analysis can lead to the development of customized therapies for a specific disease, especially where none was previously identified.
However, one limitation of patient tailored therapies is that they rely on whole genome sequencing (WGS) data; WGS is a slow process that up until recently could not be adapted for rapid diagnosis and treatment. Addressing this issue, Miller et al. (2015) developed a WGS method called STATseq that provides a 26 hour turnaround for molecular diagnosis of pediatric genetic diseases, such as severe combined immune deficiency (SCID; T-cell negative, auto recessive).
Specifically for cancer treatment however, Ryall et al. (2015) have capitalized on the knowledge that uncontrolled cell growth is triggered by mutations or copy number alterations of kinases. This insight was used to develop a tool called the Kinase Addiction Ranker (KAR). The algorithm identifies kinase dependency in cancer cells and tumor samples and recommends a combination of existing kinase inhibitor drugs that provides the highest chance for treatment success. Such a program would be a useful asset to clinicians, once converted into a quick and easy-to-use format.
This certainly allows us to envisage a future where the power of sequencing technology advancements and precision medicine are combined to generate tailor-made therapies. For instance, what if the STATseq and KAR technologies could be developed into an app that would enable your physician to create a specific treatment plan uniquely for you? It is clear that the research efforts in science and technology will ultimately lead to a future where precision medicine is the standard of care.
If characterizing kinase mediated signaling or abnormalities is a part of your cancer research strategy, why not try out the new Bio-Rad selection tool to help you choose the optimal kinase antibody for your specific application.
Interested in learning more about the counterpart of kinases? Then check out our recent blog post on the dangers of natural phosphatase inhibitors.
Beaudet et al. (1999) 1998 ASHG presidential address. Making genomic medicine a reality. Am J Hum Genet, 64:1-13.
Corkey et al. (2009) Epidermal growth factor receptor as a potential therapeutic target in triple negative breast cancer. Ann Oncol, 20: 862-867.
Hui-Fang Teng et al. (2011) Mutations in the epidermal growth factor receptor (EGF R) gene in triple negative breast cancer: possible implications for targeted therapy. Breast Cancer Res, 13:R35.
Kwan et al. (2009) Epidemiology of breast cancer subtypes in two prospective cohort studies of breast cancer survivors. Breast Cancer Res, 11:R31.
Miller et al. (2015) A 26-hour system of highly sensitive whole genome sequencing for emergency management of genetic diseases. Genome Med, 7:100.
Ryall et al. (2015) Identifying kinase dependency in cancer cells by integrating high-throughput drug screening and kinase inhibition data. Bioinformatics, doi: 10.1093/bioinformatics/btv427.